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WO2025156910A1 - Procédé et appareil de surveillance de performance, dispositif et support de stockage - Google Patents

Procédé et appareil de surveillance de performance, dispositif et support de stockage

Info

Publication number
WO2025156910A1
WO2025156910A1 PCT/CN2024/142031 CN2024142031W WO2025156910A1 WO 2025156910 A1 WO2025156910 A1 WO 2025156910A1 CN 2024142031 W CN2024142031 W CN 2024142031W WO 2025156910 A1 WO2025156910 A1 WO 2025156910A1
Authority
WO
WIPO (PCT)
Prior art keywords
model
information
parameter
terminal device
performance monitoring
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/CN2024/142031
Other languages
English (en)
Chinese (zh)
Inventor
李骞睿
黄秋萍
骆亚娟
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Datang Mobile Communications Equipment Co Ltd
Original Assignee
Datang Mobile Communications Equipment Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Datang Mobile Communications Equipment Co Ltd filed Critical Datang Mobile Communications Equipment Co Ltd
Publication of WO2025156910A1 publication Critical patent/WO2025156910A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/04—Arrangements for maintaining operational condition
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • G06N5/045—Explanation of inference; Explainable artificial intelligence [XAI]; Interpretable artificial intelligence

Definitions

  • the present disclosure relates to the field of communication technology, and more particularly to a performance monitoring method, apparatus, device, and storage medium.
  • AI models can be used to improve the performance of communication systems.
  • performance monitoring of AI models is particularly important.
  • base stations can periodically send reference signals related to AI models to terminal devices, thereby periodically enabling performance monitoring of the AI models.
  • AI models do not require frequent performance monitoring, which results in a waste of communication resources.
  • the present disclosure relates to a performance monitoring method, apparatus, device, and storage medium, which solve the technical problem of wasted communication resources in performance monitoring of AI models.
  • the present disclosure provides a performance monitoring method, applied to a terminal device, the method comprising:
  • the performance monitoring request includes an identification of the AI model and/or an identification of a function.
  • the performance monitoring request includes an identifier of an applicable model, an identifier of an applicable function, and an identifier of an applicable scenario corresponding to the performance monitoring request.
  • the performance monitoring request includes a start time of performance monitoring and/or an end time of performance monitoring.
  • the performance monitoring request includes target information, which is related to the performance indicators of the AI model and/or the measurement values of the AI model.
  • the target information includes at least one of the following:
  • CSI channel state information
  • the reference signal received power related to the AI model
  • the trigger event includes a first event related to the AI model and/or function and a second event related to the performance of the terminal device.
  • the first event when the AI model is not activated, not selected, or not paired, the first event includes at least one of the following:
  • the first event is an event determined based on at least one of the following information:
  • the update information of the AI model and/or the transfer information of the AI model are provided.
  • the first event is an event determined based on a final key performance indicator of the measurement, and the first event includes at least one of the following:
  • a first parameter in the final key performance indicator is less than a threshold corresponding to the first parameter, and the first parameter is proportional to the performance of the AI model;
  • a second parameter in the final key performance indicator is greater than a threshold corresponding to the second parameter, and the second parameter is inversely proportional to the performance of the AI model;
  • the number of times that the first parameter in the final key performance indicator is less than the threshold corresponding to the first parameter within the first time period is greater than the first threshold
  • the number of times that the second parameter in the final key performance indicator is greater than a threshold corresponding to the second parameter within the first duration is greater than the first threshold
  • a deviation between the parameter in the final key performance indicator and a first target value is greater than a second threshold, and the first target value is a target value corresponding to the parameter in the final key performance indicator.
  • the first event is an event determined based on statistical information and/or distribution information of measured CSI-related information, and the first event includes at least one of the following:
  • a third parameter in the statistical information and/or distribution information of the measured CSI-related information is greater than a threshold corresponding to the third parameter, and the third parameter is inversely proportional to the performance of the AI model;
  • a fourth parameter in the statistical information and/or distribution information of the measured CSI-related information is less than a threshold corresponding to the fourth parameter, and the fourth parameter is proportional to the performance of the AI model;
  • the number of times that a third parameter in the statistical information and/or distribution information of the measured CSI-related information within the second duration is greater than a threshold corresponding to the third parameter is greater than the third threshold;
  • the number of times that a fourth parameter in the statistical information and/or distribution information of the measured CSI-related information within the second duration is less than a threshold corresponding to the fourth parameter is greater than the third threshold
  • a deviation between the parameter in the statistical information and/or distribution information of the measured CSI-related information and the second target value is greater than a fourth threshold, and the second target value is a target value corresponding to the parameter in the statistical information and/or distribution information of the measured CSI-related information.
  • the first event is an event determined based on statistical information and/or distribution information of CSI-related information inferred by the AI model, and the first event includes at least one of the following:
  • a fifth parameter in the statistical information and/or distribution information of the inferred CSI-related information is greater than a threshold corresponding to the fifth parameter, and the fifth parameter is inversely proportional to the performance of the AI model;
  • a sixth parameter in the statistical information and/or distribution information of the inferred CSI-related information is less than a threshold corresponding to the sixth parameter, and the sixth parameter is proportional to the performance of the AI model;
  • the number of times that a fifth parameter in the statistical information and/or distribution information of the inferred CSI-related information within the third duration is greater than a threshold corresponding to the fifth parameter is greater than the fifth threshold;
  • a deviation between the parameters in the statistical information and/or distribution information of the inferred CSI-related information and the third target value is greater than a sixth threshold, and the third target value is a target value corresponding to the parameters in the statistical information and/or distribution information of the inferred CSI-related information.
  • the first event is an event determined based on a generalization indicator of the AI model, and the first event includes at least one of the following:
  • a duration during which a parameter in a performance indicator of the received reference signal is less than a seventh threshold and greater than an eighth threshold
  • the number of times that the parameter in the performance indicator of the received reference signal is less than the seventh threshold within the fourth time duration is greater than the ninth threshold
  • the quasi co-location information of the received reference signal changes
  • the duration during which the parameter in the measured speed information is greater than the tenth threshold is greater than the eleventh threshold
  • the number of times that the parameter in the measured speed information is greater than the tenth threshold within the fifth time period is greater than the twelfth threshold
  • the distance difference between the two positions measured at the sixth time interval is greater than the thirteenth threshold
  • the operating center frequency, subcarrier and/or bandwidth configuration changes.
  • the first event is an event determined based on update information and/or model transfer information of the AI model, and the first event includes at least one of the following:
  • no performance monitoring related indication is received within a seventh time period.
  • the second event is an event determined based on upper layer signaling, and the second event includes at least one of the following:
  • a handover occurs in the cell where the cell is located
  • a first monitoring quantity is greater than a fourteenth threshold, and the first monitoring quantity is inversely proportional to the performance of the terminal device;
  • the second monitoring amount is less than a fifteenth threshold, and the first monitoring amount is proportional to the performance of the terminal device;
  • a first parameter of a final key performance indicator at a future moment predicted based on the final key performance indicator at the current moment is less than a threshold corresponding to the first parameter
  • a second parameter of the final key performance indicator at a future moment predicted based on the final key performance indicator at the current moment is greater than a threshold corresponding to the second parameter.
  • the threshold, duration, and target value are based on protocol pre-definition and/or network-side configuration and/or a mixture of protocol pre-definition and network-side configuration.
  • the CSI-related information includes the CSI information, beam information and positioning information.
  • the response to the performance monitoring request includes a reply or indication of the performance monitoring request.
  • sending a performance monitoring request of an AI model to a network device includes:
  • the performance monitoring request is sent to the network device based on the physical uplink control channel PUCCH resources or the physical uplink shared channel PUSCH resources of the reserved period.
  • the present disclosure provides another performance monitoring method, applied to a network device, the method comprising:
  • the sending of the response to the performance monitoring request to the terminal device includes:
  • a response to the performance monitoring request is sent to the terminal device.
  • sending a response to the performance monitoring request to the terminal device includes:
  • a response to the scheduling request SR is sent to the terminal device, where the response to the scheduling request SR includes downlink control information DCI for uplink transmission resource allocation, and the downlink control information DCI is encrypted based on the radio network temporary identifier RNTI of the terminal device.
  • the present disclosure provides a performance monitoring device, which is applied to a terminal device.
  • the performance monitoring device includes a sending module, a receiving module, and a monitoring module, wherein:
  • the sending module is used to send a performance monitoring request of the AI model to the network device according to the trigger event;
  • the receiving module is used to receive a response to the performance monitoring request sent by the network device
  • the monitoring module is used to monitor the performance of the AI model based on the response to the performance monitoring request.
  • the present disclosure provides another performance monitoring device, applied to a network device, the performance monitoring device including a receiving module and a sending module, wherein:
  • the receiving module is configured to receive a performance monitoring request for an AI model sent by a terminal device, where the performance monitoring request is determined by the terminal device based on a triggering event;
  • the sending module is used to send a response to the performance monitoring request to the terminal device.
  • the present disclosure provides a terminal device, including a memory, a transceiver, and a processor:
  • the memory is used to store computer programs
  • the transceiver is used to send and receive data under the control of the processor
  • the processor is configured to read the computer program in the memory and perform the following operations:
  • the present disclosure provides a network device, including a memory, a transceiver, and a processor:
  • the memory is used to store computer programs
  • the transceiver is used to send and receive data under the control of the processor
  • the processor is configured to read the computer program in the memory and perform the following operations:
  • the present disclosure provides a processor-readable storage medium, wherein the processor-readable storage medium stores a computer program, and the computer program is used to enable a processor to execute the method described in the first aspect or the method described in the second aspect.
  • the present disclosure provides a performance monitoring method, apparatus, equipment and storage medium, in which a terminal device can send a performance monitoring request of an AI model to a network device based on a trigger event, receive a response to the performance monitoring request sent by the network device, and perform performance monitoring on the AI model based on the response to the performance monitoring request.
  • the terminal device can avoid frequently (periodically or semi-periodically) enabling the performance monitoring of the AI model, thereby reducing the communication resources required for monitoring the AI model, and in the event of AI model failure, AI model performance degradation and other events, the terminal device can promptly enable the performance monitoring of the AI model, thereby improving the timeliness of the AI model performance monitoring, and thus improving the stability of the communication.
  • This method can also be used in conjunction with a method for enabling model monitoring aperiodically, periodically or semi-periodically, to achieve the purpose of quickly and flexibly performing AI model performance monitoring based on the available communication resources in the system.
  • FIG1 is a schematic diagram of a communication scenario provided by an embodiment of the present disclosure.
  • FIG2 is a flow chart of a performance monitoring method provided by an embodiment of the present disclosure.
  • FIG3 is a schematic diagram of a method for monitoring the performance of an AI model provided by an embodiment of the present disclosure
  • FIG4 is a schematic diagram of another method for monitoring the performance of an AI model provided by an embodiment of the present disclosure.
  • FIG5 is a schematic diagram of another method for monitoring the performance of an AI model provided by an embodiment of the present disclosure.
  • FIG6 is a schematic diagram of a method for triggering a first event provided by an embodiment of the present disclosure
  • FIG7 is a schematic diagram of another method for triggering a first event provided by an embodiment of the present disclosure.
  • FIG8 is a schematic diagram of a method for sending a monitoring request provided by an embodiment of the present disclosure.
  • FIG9 is a schematic diagram of another method for sending a monitoring request according to an embodiment of the present disclosure.
  • FIG10 is a schematic diagram of another method for sending a monitoring request according to an embodiment of the present disclosure.
  • FIG11 is a schematic diagram of another method for sending a monitoring request according to an embodiment of the present disclosure.
  • FIG12 is a schematic diagram of another method for sending a monitoring request according to an embodiment of the present disclosure.
  • FIG13 is a schematic diagram of a method for sending a monitoring request according to an embodiment of the present disclosure.
  • FIG14 is a schematic diagram of a method for sending a response to a monitoring request to a terminal device according to an embodiment of the present disclosure
  • FIG15 is a schematic structural diagram of a performance monitoring device provided by an embodiment of the present disclosure.
  • FIG16 is a schematic diagram of the structure of another performance monitoring device provided by an embodiment of the present disclosure.
  • FIG17 is a schematic structural diagram of a terminal device provided in an embodiment of the present disclosure.
  • FIG18 is a schematic structural diagram of a network device provided in an embodiment of the present disclosure.
  • the term "and/or” describes the association relationship between associated objects, indicating that three relationships can exist.
  • a and/or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.
  • the character "/" generally indicates that the associated objects are in an "or” relationship.
  • the term “plurality” refers to two or more than two, and other quantifiers are similar thereto.
  • the embodiments of the present disclosure provide a performance monitoring method, apparatus, device, and storage medium.
  • a terminal device can enable monitoring of an AI model based on a triggering event. This eliminates the need for network devices to frequently enable monitoring of the AI model's performance, thereby reducing the consumption of communication resources and improving the timeliness of monitoring the AI model's performance.
  • the method and the device are based on the same application concept. Since the principles of solving problems by the method and the device are similar, the implementation of the device and the method can refer to each other, and the repeated parts will not be repeated.
  • LTE Long Term Evolution
  • FDD Frequency Division Duplex
  • TDD Time Division Duplex
  • LTE-A Long Term Evolution Advanced
  • UMTS Universal Mobile Telecommunication System
  • WiMAX Worldwide Interoperability for Microwave Access
  • NR 5G New Radio
  • LTE Long Term Evolution
  • EPC Evolved Packet Core
  • 5GC 5G Core Network
  • the terminal device involved in the embodiments of the present disclosure may be a device that provides voice and/or data connectivity to a user, a handheld device with wireless connection function, or other processing device connected to a wireless modem.
  • the name of the terminal device may also be different.
  • the terminal device may be called a user equipment (UE).
  • the wireless terminal device may be a USB storage device, other personal computer memory devices and a dongle, and may also communicate with one or more core networks (CN) via a radio access network (RAN).
  • CN core networks
  • RAN radio access network
  • the wireless terminal device may be a mobile terminal device, such as a mobile phone (or "cellular" phone) and a computer with a mobile terminal device.
  • it may be a portable, pocket-sized, handheld, computer-built-in or vehicle-mounted mobile device that exchanges language and/or data with a radio access network.
  • PCS Personal Communication Service
  • SIP Session Initiated Protocol
  • WLL Wireless Local Loop
  • PDA Personal Digital Assistants
  • MTC Machine-type Communication terminal devices
  • Wireless terminal devices may also be referred to as systems, subscriber units, subscriber stations, mobile stations, mobile stations, remote stations, access points, remote terminals, access terminals, user terminals, user agents, user devices, and wireless access points and routers/modems that meet the limitations of this definition, but are not limited in the embodiments of the present disclosure.
  • the network device involved in the embodiments of the present disclosure may be a base station, which may include multiple cells providing services to terminals.
  • a base station may also be called an access point, or may be a device in an access network that communicates with a wireless terminal device over the air interface through one or more sectors, or may be called another name.
  • the network device may be used to interchange received air frames with Internet Protocol (IP) packets, acting as a router between the wireless terminal device and the rest of the access network, where the rest of the access network may include an Internet Protocol (IP) communication network.
  • IP Internet Protocol
  • the network device may also coordinate the attribute management of the air interface.
  • the network device involved in the embodiments of the present disclosure may be an evolved network device (eNB or e-NodeB) in a long-term evolution (LTE) system, a 5G base station (gNB) in a next-generation 5G system, or a home evolved Node B (HeNB), a relay node, a femto base station, a pico base station, a network test device, etc., and is not limited in the embodiments of the present disclosure.
  • network devices may include centralized unit (CU) nodes and distributed unit (DU) nodes, and the centralized unit and the distributed unit may also be geographically separated.
  • FIG1 is a schematic diagram of a communication scenario provided by an embodiment of the present disclosure.
  • a terminal device and a network device are included, and both the terminal device and the network device can serve as users of the AI model.
  • the monitoring party of the AI model can perform life cycle management (LCM) operations related to the AI model based on the performance monitoring results of the AI model. For example, when the current AI model is invalid, the monitoring party of the AI model can perform operations such as model deactivation, model switching, model rollback, and model update, thereby ensuring stable communication.
  • LCM life cycle management
  • network devices can send reference signals related to AI models to terminal devices, thereby enabling performance monitoring of the AI models.
  • the AI model is a model that processes Channel State Information (CSI)
  • the network device can send CSI-related reference signals to the terminal device non-periodically, periodically, or semi-periodically.
  • the terminal device can measure the actual CSI based on the reference signal and process (compress and restore) the actual CSI through the AI model.
  • the performance of the current AI model can then be monitored based on the actual CSI and the CSI output by the AI model.
  • the performance of the AI model is relatively stable. Therefore, the AI model does not require frequent performance monitoring, which results in a waste of communication resources.
  • the network device cannot enable performance monitoring of the AI model in a timely manner, resulting in low communication stability.
  • the embodiment of the present disclosure provides a performance monitoring method, wherein the terminal device can send a performance monitoring request of the AI model to the network device according to the trigger event, wherein the performance monitoring request can include the identifier of the AI model and/or the identifier of the function, and the trigger event can include events related to the AI model and/or the function, and events related to the performance of the terminal device.
  • the terminal device can receive the response to the performance monitoring request sent by the network device, and perform performance monitoring on the AI model according to the response to the performance monitoring request.
  • the terminal device can promptly start performance monitoring of the AI model, thereby ensuring the stability of communication quality. Moreover, since the terminal device can start performance monitoring of the AI model based on the trigger event, there is no need for the network device to frequently (periodically or semi-periodically) start performance monitoring of the AI model, thereby saving communication resources.
  • FIG2 is a flow chart of a performance monitoring method provided by an embodiment of the present disclosure. Referring to FIG2 , the method flow includes:
  • the terminal device sends a performance monitoring request of the AI model to the network device based on a trigger event.
  • the trigger event can be used to trigger the terminal device to send a performance monitoring request for the AI model to the network device. For example, when the terminal device detects the occurrence of a trigger event, the terminal device can send a performance monitoring request for the AI model to the network device.
  • the triggering event may include a first event related to the AI model and/or function and a second event related to the performance of the terminal device.
  • the event may be the first event
  • the block error rate is greater than the preset block error rate
  • the event may be the first event
  • the first event may be any event related to the AI model and/or function, and the embodiments of the present disclosure do not limit this.
  • the event may be the second event
  • the event may be the second event
  • the second event may be any event related to the performance of the terminal device, and the embodiments of the present disclosure do not limit this.
  • the AI model may be a model for processing CSI information, the AI model may be a model for processing beam information, or the AI model may be a model for processing positioning information, which is not limited in the embodiments of the present disclosure.
  • the performance monitoring request can be used to request performance monitoring of the AI model.
  • the performance monitoring request can request performance monitoring of the AI model, and the monitoring request can also trigger the activation of the performance monitoring of the AI model.
  • performance monitoring can include model-based monitoring (model-based performance monitoring, i.e., performance monitoring) and function-based monitoring (function can be the function of the AI model or the AI function in communication, which is not limited in the embodiments of this disclosure).
  • the terminal device can perform lifecycle management of the AI model based on the results of performance monitoring.
  • the performance monitoring request may include an identifier of the AI model and/or an identifier of the function.
  • the performance monitoring request may include an identification number (ID) of the AI model
  • the performance monitoring request may include an ID of the function
  • the performance monitoring request may include both an ID of the AI model and an ID of the function, which is not limited in the present embodiment.
  • the terminal device requests performance monitoring of AI model 1; if the performance monitoring request sent by the terminal device to the network device includes the identifier of AI model 2, the terminal device requests performance monitoring of AI model 2. For example, if the performance monitoring request sent by the terminal device to the network device includes the identifier of function A, the terminal device requests performance monitoring of function A; if the monitoring request sent by the terminal device to the network device includes the identifier of function B, the terminal device requests performance monitoring of function B. In this way, the terminal device can accurately perform performance monitoring on AI models and/or functions.
  • the performance monitoring request may include an identifier indicating the applicable model, an identifier indicating the applicable function, and an identifier indicating the applicable scenario to which the performance monitoring request corresponds.
  • the performance monitoring request may include an identifier indicating the function of the AI model to which the performance monitoring request corresponds, and an identifier indicating the applicable scenario to which the performance monitoring request corresponds.
  • the performance monitoring request may include the start time of performance monitoring and/or the end time of performance monitoring.
  • the performance monitoring request may include the start time of performance monitoring, the performance monitoring request may also include the end time of performance monitoring, and the performance monitoring request may also include the start time of performance monitoring and the end time of performance monitoring.
  • the terminal device may start performance monitoring of the AI model at the start time; if the performance monitoring request includes the end time of performance monitoring, the terminal device may stop performance monitoring of the AI model at the end time; if the performance monitoring request includes the start time of performance monitoring and the end time of performance monitoring, the terminal device may perform performance monitoring on the AI model during the period between the start time and the end time. In this way, the terminal device can flexibly perform performance monitoring on the AI model.
  • the network device can execute the performance monitoring process of the AI model when it receives the performance monitoring request (the performance monitoring process of the AI model starts when the network device sends a reference signal related to the AI model, but in this disclosure, starting the performance monitoring process of the AI model is determined by the performance monitoring request sent by the terminal device).
  • the terminal device can stop performance monitoring after starting the performance monitoring for a preset period of time.
  • the terminal device can also stop performance monitoring at any time. This embodiment of the present disclosure does not limit this.
  • start time and the end time in the embodiment of the present disclosure may be absolute time or offset time, and the embodiment of the present disclosure is not limited to this.
  • the performance monitoring request may include target information.
  • the target information may be related to the performance indicators of the AI model and/or the measurement values of the AI model.
  • the target information may include at least one of the following: intermediate key performance indicators related to the AI model, final key performance indicators related to the AI model, statistical information of information related to channel state information (CSI), distribution information of information related to CSI, speed information, and location information.
  • CSI channel state information
  • intermediate key performance indicators related to the AI model can indicate the performance of the AI model.
  • the terminal device can obtain real CSI, compress and restore the real CSI based on the AI model, and obtain predicted CSI.
  • the terminal device can calculate the intermediate KPI (performance metric calculation) of the AI model based on the real CSI and the predicted CSI, and determine the inference precision and accuracy of the AI model based on the intermediate KPI.
  • the final key performance indicators (Eventual KPIs) related to the AI model can be used to indicate the performance of the AI model.
  • the final KPIs may include parameters such as throughput, hypothetical throughput, hypothetical block error rate (BLER), and block error rate. These parameters can accurately reflect the quality of the communication system.
  • the terminal device can determine the performance of the AI model based on the quality of the communication system. For example, if the throughput is low, it indicates that the current communication system quality is poor and the performance of the AI model is low.
  • the information related to the channel state information CSI may include CSI information, beam information and positioning information.
  • the information related to the CSI may also include any information that may affect the CSI, which is not limited in the embodiments of the present disclosure.
  • the statistical information of CSI-related information may include average delay, delay spread, Doppler shift, Doppler spread and other information
  • the CSI-related distribution information may include LOS (Line-of-Sight) distribution, NLOS (Non-Line-of-Sight) distribution, etc., which is not limited to the embodiments of the present disclosure.
  • the AI model-related reference signal received power may be the power strength of a reference signal received by a terminal device.
  • RSRP may measure the signal strength of a terminal device or a cell.
  • the reference signal may be a CSI-related reference signal.
  • the AI model processes beam information the reference signal may be a beam-related reference signal.
  • the AI model processes positioning information the reference signal may be a positioning-related reference signal.
  • the AI model-related reference signal received quality may be the quality of a reference signal received by a terminal device.
  • the terminal device may determine the signal quality based on the RSRQ.
  • the Signal to Interference plus Noise Ratio can be the ratio of the strength of the received useful signal to the strength of the received interference signal (noise plus interference). For example, based on the SINR, a terminal device can accurately determine the impact of interference other than noise on the signal.
  • the speed information may include the absolute value of the linear velocity, the absolute value of the angular velocity, the linear velocity acceleration, and the angular velocity acceleration of the terminal device.
  • the speed information may include any information related to the speed of the terminal device, which is not limited in the embodiments of the present disclosure.
  • the location information may be information related to the location of the terminal device.
  • the location information may be the current location of the terminal device, or the location difference between two locations measured by the terminal device within a period of time, which is not limited in the present embodiment.
  • the performance indicators and measurement values of the above-mentioned target information can be L1 (layer 1) or L3 (layer 3) filtered, and the embodiments of the present disclosure are not limited to this.
  • the network device can determine whether to enable performance monitoring of the AI model based on the target information.
  • the judgment process of whether the performance monitoring of the AI model is enabled is determined by the network device, the initiation of the judgment process is determined by the performance monitoring request sent by the terminal device. Therefore, the network device does not need to frequently enable performance monitoring of the AI model, thereby saving communication resources.
  • the terminal device may send a performance monitoring request of the AI model to the network device according to the following three feasible implementation methods:
  • a scheduling request (SR) based on uplink control information (UCI) is sent to network devices to monitor performance. For example, if a terminal device uses UCI reporting, it can send the SR and the physical uplink shared channel (PUSCH) information required for uplink transmission (e.g., the number of reported parameters).
  • UCI uplink control information
  • PUSCH physical uplink shared channel
  • a performance monitoring request is sent to a network device based on Media Access Control Element (MAC-CE) signaling.
  • MAC-CE Media Access Control Element
  • a terminal device may send a performance monitoring request using MAC-CE signaling, which may also include target information.
  • the terminal device can report the performance monitoring request on the reserved periodic PUCCH or PUSCH resources based on the Layer 1 resource pre-configuration.
  • PUCCH Physical Uplink Control Channel
  • PUSCH Physical Uplink Control Channel
  • S202 The network device sends a response to the performance monitoring request to the terminal device.
  • the network device after the network device receives the performance monitoring request sent by the terminal device, it can respond to the performance monitoring request.
  • the response to the performance monitoring request may include a reply or indication of the monitoring request.
  • the response to the performance monitoring request may include agreeing to turn on the performance monitoring of the AI model and/or agreeing to activate the performance monitoring of the AI model, and the response to the performance monitoring request may include refusing to turn on the performance monitoring of the AI model and/or refusing to activate the performance monitoring of the AI model.
  • the network device After the network device determines the response to the monitoring request, it may send the response to the monitoring request to the terminal device.
  • the network device can send a response to the performance monitoring request to the terminal device according to the following feasible implementation method: receive a scheduling request SR sent by the terminal device, and send a response to the scheduling request SR sent by the terminal device.
  • the response to the scheduling request SR includes downlink control information (DCI) for uplink transmission resource allocation, and the downlink control information DCI is scrambled based on the terminal device's radio network temporary identifier (RNTI).
  • DCI downlink control information
  • RNTI radio network temporary identifier
  • a terminal device sends a performance monitoring request for an AI model to a network device based on a UCI-based retrieval request SR
  • the terminal device can send the SR on the PUCCH or random access channel (RACH).
  • RACH random access channel
  • the network device can send an uplink grant as a response to the scheduling request SR.
  • the uplink grant can include the DCI for uplink transmission resource allocation, which can be scrambled based on the terminal device's RNTI (in order to distinguish the network-side DCI response to other UE uplink transmission scheduling requests, the terminal device's RNTI can be a newly defined dedicated RNTI). If the terminal device receives the same RNTI and can descramble the DCI, the terminal device can determine that the network device has received the performance monitoring request sent by the terminal device.
  • the network device's response information is a DCI format for scheduling PUSCH transmission.
  • the DCI format may include the same Hybrid Automatic Repeat reQuest (HARQ) process number as the PUSCH of the MAC-CE in which the terminal device sends the performance monitoring request, as well as a flipped Network Device Interface (NDI) field.
  • HARQ Hybrid Automatic Repeat reQuest
  • NDI Network Device Interface
  • the terminal device detects the DCI, it determines that the network device has received the monitoring request.
  • the NDI field may be a 1-bit indication field.
  • the DCI responded by the base station may be a DCI format for scheduling PUSCH transmission, wherein the HARQ process ID of the newly scheduled PUSCH is different from the HARQ process ID of the PUSCH to which the terminal device previously sent MAC-CE (the HARQ process IDs may be accumulated). Therefore, if the terminal device detects that the DCI has the same HARQ process ID as the PUSCH to which the MAC-CE was previously sent, it means that the DCI responded by the base station corresponds to the MAC-CE sent by the terminal device.
  • the terminal device performs performance monitoring on the AI model based on the response to the performance monitoring request.
  • the terminal device can receive the response to the performance monitoring request sent by the network device, and perform performance monitoring on the AI model based on the response to the performance monitoring request. For example, if the response to the performance monitoring request is to agree to enable performance monitoring, the terminal device can receive the reference signal of the AI model sent by the network device, and then perform performance monitoring on the AI model. If the response to the monitoring request is to refuse to enable performance monitoring, the terminal device can stop enabling performance monitoring or continue to send performance monitoring requests to the network device. This embodiment of the present disclosure is not limited to this.
  • a terminal device after a terminal device sends a performance monitoring request for an AI model to a network device, if the terminal device does not receive a response to the performance monitoring request within a certain period of time, the terminal device may resend the performance monitoring request to the network device.
  • the performance monitoring request may be a regenerated performance monitoring request or an already generated performance monitoring request, which is not limited in the embodiments of the present disclosure. For example, if a terminal device sends a performance monitoring request to a network device and does not receive a response to the performance monitoring request within 10 seconds, the terminal device may resend the performance monitoring request to the network device.
  • the terminal device can send the performance monitoring of the AI model to the network device according to the triggering event. For example, after the terminal device sends a performance monitoring request to the network device three times and has not received a response to the performance monitoring request, the terminal device can regenerate the performance monitoring request (the monitoring request can include more target information, etc.) according to the triggering event and send the performance monitoring request to the network device. In this way, the accuracy and timeliness of the performance monitoring of the AI model can be improved.
  • the AI model is a model for processing CSI.
  • Figure 3 is a schematic diagram of a method for performance monitoring of an AI model provided by an embodiment of the present disclosure.
  • the part of the model that compresses CSI is located in the terminal device, and the part of the model that decompresses CSI is located in the network device.
  • the network device performs performance monitoring of the AI model, please refer to Figure 3, including the terminal device and the network device.
  • the network device can send a reference signal related to the channel state information to the terminal device.
  • the terminal device can obtain the measured channel state information based on the reference signal, and the terminal device can compress the measured channel state information based on the compressed part of the model.
  • the terminal device can send compressed channel state information to the network device.
  • the network device can then decompress the compressed channel state information based on the decompressed portion of the model to obtain decompressed channel state information.
  • the terminal device can also send measured channel state information to the network device.
  • the network device can then determine the performance of the model based on the measured and decompressed channel state information. This allows the network device to calculate intermediate KPIs based on the measured and decompressed CSI, and then determine the performance of the AI model based on the intermediate KPIs, thereby improving the accuracy of the model's performance monitoring.
  • Figure 4 is a schematic diagram of another method for performance monitoring of an AI model provided by an embodiment of the present disclosure.
  • the part of the model that compresses CSI is located in the terminal device, and the part of the model that decompresses CSI is located in the network device.
  • the terminal device performs performance monitoring of the AI model, please refer to Figure 4, including the terminal device and the network device.
  • the network device can send a reference signal related to the channel state information to the terminal device.
  • the terminal device can obtain the measured channel state information based on the reference signal, and the terminal device can compress the measured channel state information based on the compressed part of the model.
  • the terminal device can send compressed channel state information to the network device.
  • the network device can then decompress the compressed channel state information based on the decompressed portion of the model to obtain decompressed channel state information.
  • the network device can then send the decompressed channel state information to the terminal device, which can then determine the performance of the model based on the measured and decompressed channel state information.
  • This allows the terminal device to calculate intermediate KPIs based on the measured and decompressed CSI, and then determine the performance of the AI model based on these intermediate KPIs, thereby improving the accuracy of the model's performance monitoring.
  • Figure 5 is a schematic diagram of another method for monitoring the performance of an AI model provided by an embodiment of the present disclosure.
  • the part of the model that compresses CSI is located in the terminal device, and the part of the model that decompresses CSI is located in the network device.
  • the terminal device monitors the AI model, and the terminal device also includes a replacement model for the part of the model that decompresses CSI.
  • the network device can send a reference signal related to the channel state information to the terminal device.
  • the terminal device can obtain the measured channel state information based on the reference signal, and the terminal device can compress the measured channel state information based on the compressed part of the model.
  • the terminal device can send compressed channel state information to the network device.
  • the network device can then decompress the compressed channel state information based on the decompressed portion of the model to obtain decompressed channel state information.
  • the terminal device can then decompress the compressed channel state information based on the alternative model and determine the model's performance based on the measured and decompressed channel state information. This allows the terminal device to calculate intermediate KPIs based on the measured and decompressed CSI, and further determine the performance of the AI model based on the intermediate KPIs, thereby improving the accuracy of the model's performance monitoring.
  • sequence numbers 1, 2, ..., 7 are only used to illustrate the steps, and do not limit the order in which the steps are executed.
  • the disclosed embodiment provides a performance monitoring method, in which a terminal device can send a performance monitoring request of an AI model to a network device according to a triggering event, wherein the triggering event may include a first event related to the AI model and/or function, and a second event related to the performance of the terminal device.
  • the monitoring request may include an identifier of the AI model, an identifier of the function of the AI model, a start time and an end time of performance monitoring, and target information related to the performance indicators and measurement values of the AI model.
  • the terminal device can receive a response to the performance monitoring request sent by the network device, and perform performance monitoring on the AI model according to the response to the performance monitoring request. In this way, since the terminal device can start the performance monitoring process, there is no need for the network device to frequently (periodically or semi-periodically) start performance monitoring of the AI model, thereby saving communication resources.
  • the first event may include at least one of the following: sending the identification of the supported AI model and/or the identification of the function, receiving the identification of the AI model and/or the identification of the function supported by the cell covered by the current base station.
  • FIG6 is a schematic diagram of a method for triggering a first event provided by an embodiment of the present disclosure.
  • the first event is the terminal device sending an identifier of a supported AI model and/or an identifier of a function.
  • the method flow includes:
  • the terminal device sends an identifier of an AI model and/or an identifier of a function supported by the terminal device to a network device.
  • the terminal device can send a performance monitoring request for the AI model when turning on AI model activation, AI model selection or AI model pairing.
  • the first event may be an event of sending an identifier of a supported AI model and/or an identifier of a function.
  • the terminal device may send an identifier of an AI model that the terminal device can currently support (or an identifier of a function) to the network device, and the network device may determine the activated AI model based on the identifier of the AI model.
  • the terminal device sends to the network device identifiers of AI models supported by the terminal device, including: an identifier of AI model 1, an identifier of AI model 2, and an identifier of AI model 3. If the network device supports AI model 1 and AI model 2, the network device may activate AI model 1 and AI model 2.
  • the terminal device can send the identification of the supported AI model and/or the identification of the function to the network device.
  • the terminal device can also send the identification of the supported AI model and/or the identification of the function to the network device in any feasible scenario.
  • the embodiments of the present disclosure are not limited to this.
  • the terminal device sends a performance monitoring request to the network device.
  • the terminal device when the terminal device sends the identifier of the supported AI model and/or the identifier of the function to the network device, the terminal device can determine that a trigger event has occurred, and the terminal device can send a performance monitoring request for the AI model to the network device.
  • the terminal device sends the identifier of the supported AI model to the network device
  • the network device can send the identifier of the activated AI model to the AI model
  • the terminal device can send a performance monitoring request to the network device.
  • the monitoring request can include the identifier of the activated AI model. In this way, when the AI model is activated, the terminal device can start performance monitoring of the AI model, thereby improving the stability of communication.
  • the performance monitoring request may include the start time of performance monitoring and/or the end time of performance monitoring.
  • the terminal device may instruct the network model to execute the performance monitoring process of the AI model after the AI model is activated, selected, or paired for a period of time, thereby improving the accuracy of the performance monitoring of the AI model.
  • the performance monitoring request may also carry target information, which is not limited in the embodiments of the present disclosure.
  • the embodiment of the present disclosure provides a method for triggering a first event, sending an identifier of an AI model and/or an identifier of a function supported by a terminal device to a network device, and sending a model monitoring request to the network device.
  • the terminal device can promptly send the supported AI model and/or function to the network device, thereby activating, selecting, or pairing the AI model and/or function supported by both the terminal device and the network device.
  • the terminal device can start performance monitoring of the AI model to improve the stability of communication.
  • FIG7 is a schematic diagram of another method for triggering a first event provided by an embodiment of the present disclosure.
  • the first event is that the terminal device receives an identifier of an AI model and/or an identifier of a function supported by a cell currently covered by a base station.
  • the method flow includes:
  • the terminal device receives the identifier of the AI model and/or the identifier of the function supported by the cell currently covered by the base station.
  • the terminal device can receive the identifier of the AI model and/or the identifier of the function supported by the cell covered by the current base station. For example, when the terminal device performs cell switching, the AI model has not been activated, selected or paired. Therefore, the terminal device can determine the activated, selected or paired AI model or function, and start performance monitoring of the AI model.
  • the network device can send the identifier of the AI model supported by the cell (or the identifier of the function) to the terminal device, and the terminal device can determine the activated, selected and paired AI model based on the identifier of the AI model supported by the cell and the identifier of the AI model supported by the terminal device.
  • the network device sends to the terminal device the identifiers of the AI models supported by the cell, including: the identifier of AI model 1, the identifier of AI model 2, and the identifier of AI model 3. If the terminal device supports AI model 1 and AI model 2, the terminal device can request to activate AI model 1 and AI model 2.
  • the terminal device can receive the identifier of the AI model supported by the cell and/or the identifier of the function sent by the network device.
  • the terminal device can also receive the identifier of the AI model supported by the cell and/or the identifier of the function sent by the network device in any feasible scenario.
  • the embodiments of the present disclosure are not limited to this.
  • the terminal device sends a performance monitoring request to the network device.
  • the terminal device when the terminal device receives the identifier of the AI model and/or the identifier of the function supported by the cell covered by the current base station sent by the network device, the terminal device can determine that a trigger event has occurred, and the terminal device can send a performance monitoring request for the AI model to the network device. For example, when the terminal device receives the identifier of the AI model supported by the cell sent by the network device, the terminal device can determine the activated AI model and send the identifier of the activated AI model to the network device, and the terminal device can send a performance monitoring request for the AI model to the network device. In this way, when the AI model is activated, the terminal device can start performance monitoring of the AI model, thereby improving the stability of communication.
  • the performance monitoring request may include the identification of the AI model, the identification of the function, the start time of performance monitoring, the end time of performance monitoring and target information, which is not limited in the embodiments of the present disclosure.
  • the disclosed embodiments provide a method for triggering a first event, wherein a terminal device receives an identifier of an AI model and/or an identifier of a function supported by a cell currently covered by a base station, and the terminal device sends a performance monitoring request to a network device.
  • the terminal device when the terminal device switches cells, the terminal device can promptly activate the AI model or function supported by both the terminal device and the network device, and when the AI model or function is activated, the terminal device can start performance monitoring, thereby improving communication stability.
  • the terminal device when the AI model has been activated, selected or paired, can determine a first event based on at least one information, wherein the at least one information may include the measured final key performance indicators, statistical information and/or distribution information of the measured CSI-related information, statistical information and/or distribution information of the CSI-related information inferred by the AI model, the generalization indicator of the AI model, the update information of the AI model and/or the transmission information of the AI model.
  • the terminal device determines that the first event is triggered, the terminal device can send a performance monitoring request of the AI model to the network device.
  • the threshold, duration and target value in the embodiment of the present disclosure are based on protocol pre-definition and/or network side configuration and/or a mixture of protocol pre-definition and network side configuration.
  • FIG8 is a schematic diagram of a method for sending a performance monitoring request according to an embodiment of the present disclosure.
  • the first event is an event determined based on the final key performance indicator measured, and the method flow includes:
  • the final key performance indicator measured may be a final KPI measured by the terminal device.
  • the final KPI measured by the terminal device may include parameters such as user throughput and block error rate, which are not limited in the present embodiment.
  • the terminal device can measure the final KPI according to any feasible implementation method, and the embodiments of the present disclosure are not limited to this.
  • S802 Determine triggering of a first event based on the final key performance indicator measured.
  • the first event is an event determined based on the measured final key performance indicator, the first event includes at least one of the following:
  • the first parameter in the final key performance indicator is less than a threshold corresponding to the first parameter
  • the second parameter in the final key performance indicator is greater than the threshold corresponding to the second parameter
  • the number of times that the first parameter in the final key performance indicator within the first time period is less than the threshold corresponding to the first parameter is greater than the first threshold
  • the number of times that the second parameter in the final key performance indicator within the first time period is greater than the threshold corresponding to the second parameter is greater than the first threshold
  • the deviation between the parameter in the final key performance indicator and the first target value is greater than the second threshold.
  • the first parameter may be proportional to the performance of the AI model. For example, a larger first parameter in the final KPI indicates higher AI model performance, and a smaller first parameter in the final KPI indicates lower AI model performance.
  • the first parameter may be user throughput. A higher user throughput indicates better communication quality, which in turn indicates higher AI model performance. A lower user throughput indicates poor communication quality, which in turn indicates lower AI model performance.
  • the second parameter is inversely proportional to the performance of the AI model.
  • the second parameter can be the block error rate.
  • a large block error rate indicates poor communication quality and, in turn, low AI model performance.
  • a small block error rate indicates good communication quality and, in turn, high AI model performance.
  • the first target value is the target value corresponding to the parameter in the final key performance indicator.
  • the first target value may be the target value corresponding to throughput; if the parameter in the final KPI is block error rate, the first target value may be the target value corresponding to the block error rate.
  • first threshold, the second threshold, the first duration, the first target value, the threshold corresponding to the first parameter, and the threshold corresponding to the second parameter can be obtained by protocol pre-definition and/or network device configuration and/or a mixture of protocol pre-definition and network side configuration, or can be determined based on any other feasible implementation method, and the embodiments of the present disclosure are not limited to this.
  • the terminal device may determine that a first event has been triggered.
  • the first parameter may be throughput
  • the threshold corresponding to the throughput may be a preset throughput. If the throughput measured by the terminal device is less than the preset throughput, it indicates that the current communication quality is poor, and the terminal device triggers the first event, thereby sending a performance monitoring request for the AI model to the network device.
  • the terminal device may determine that a first event has been triggered.
  • the second parameter may be a block error rate
  • the threshold corresponding to the block error rate may be a preset block error rate. If the block error rate measured by the terminal device is greater than the preset block error rate, it indicates that the current communication quality is poor, and the terminal device triggers the first event, thereby sending a performance monitoring request for the AI model to the network device.
  • the terminal device can determine that the first event is triggered.
  • the first parameter can be throughput
  • the threshold corresponding to the throughput can be threshold 1
  • the first threshold can be threshold 2.
  • the terminal device can determine that the current communication quality is poor, the terminal device triggers the first event, and then can send a performance monitoring request for the AI model to the network device.
  • the first time period is 10 seconds
  • the threshold corresponding to the throughput is the preset throughput
  • the first threshold is 3 times. If the throughput measured by the terminal device within 10 seconds is less than the preset throughput 10 times, the terminal device can determine that the first event is triggered and send a performance monitoring request for the AI model to the network device.
  • the terminal device can determine that the first event is triggered.
  • the second parameter can be the block error rate
  • the threshold corresponding to the block error rate can be threshold 1
  • the second threshold can be threshold 2.
  • the terminal device can determine that the current communication quality is poor, the terminal device triggers the first event, and then can send a performance monitoring request for the AI model to the network device.
  • the first time period is 10 seconds
  • the threshold corresponding to the block error rate is the preset block error rate
  • the first threshold is 3 times. If the block error rate measured by the terminal device within 10 seconds is less than the preset block error rate 4 times, the terminal device can determine that the first event is triggered and send a performance monitoring request for the AI model to the network device.
  • the terminal device can determine that the first event is triggered.
  • the parameter in the final key performance indicator may include a first parameter and a second parameter, wherein each parameter has a corresponding first target value. If the deviation between the parameter in the final KPI and the first target value corresponding to the parameter is greater than the second threshold, it means that the current communication quality is poor, and the terminal device can send a performance monitoring request of the AI model to the network device.
  • the parameter of the final KPI is throughput
  • the first target value corresponding to the throughput is a preset throughput. If the difference between the throughput measured by the terminal device and the preset throughput is greater than the second threshold, the terminal device can determine that the first event is triggered and send a performance monitoring request to the network device.
  • the terminal device when the terminal device determines that the first event is currently triggered based on the final KPI, the terminal device can send a monitoring request of the AI model to the network device.
  • the disclosed embodiment provides a method for sending a monitoring request, wherein a terminal device can obtain the final key performance indicator of the measurement, and based on the final key performance indicator of the measurement, determine that the terminal device triggers a first event, and send a monitoring request to a network device.
  • the terminal device can determine the communication quality based on the final KPI, and determine whether to enable the monitoring of the AI model based on the communication quality.
  • the terminal device can promptly monitor the performance of the AI model, thereby improving the performance monitoring accuracy of the AI model and thereby improving the stability of the communication.
  • FIG9 is a schematic diagram of another method for sending a performance monitoring request according to an embodiment of the present disclosure.
  • the first event is an event determined based on statistical information and/or distribution information of measured CSI-related information.
  • the method flow includes:
  • the statistical information and/or distribution information of the measured CSI-related information may include information such as average delay and delay spread corresponding to the CSI-related information measured by the terminal device.
  • the CSI-related information may be CSI information
  • the statistical information and/or distribution information corresponding to the CSI information measured by the terminal device may include average delay, delay spread, Doppler shift, Doppler spread, LOS distribution, NLOS distribution, channel covariance matrix, and KL (Kullback-Leibler) divergence, etc. associated with the CSI information.
  • the terminal device can obtain statistical information and/or distribution information of the measured CSI-related information according to any feasible implementation method, and the embodiments of the present disclosure are not limited to this.
  • S902 Determine triggering of a first event based on statistical information and/or distribution information of measured CSI-related information.
  • the first event is an event determined based on statistical information and/or distribution information of measured CSI-related information
  • the first event includes at least one of the following:
  • a third parameter in the statistical information and/or distribution information of the measured CSI-related information is greater than a threshold corresponding to the third parameter
  • a fourth parameter in the statistical information and/or distribution information of the measured CSI-related information is less than a threshold corresponding to the fourth parameter
  • the number of times that a third parameter in the statistical information and/or distribution information of the CSI-related information measured within the second duration is greater than a threshold corresponding to the third parameter is greater than the third threshold;
  • the number of times that a fourth parameter in the statistical information and/or distribution information of the CSI-related information measured within the second duration is greater than a threshold corresponding to the fourth parameter is greater than the third threshold;
  • a deviation between a parameter in the statistical information and/or distribution information of the measured CSI-related information and the second target value is greater than a fourth threshold.
  • the third parameter may be inversely proportional to the performance of the AI model.
  • the third parameter may be average latency.
  • a larger average latency indicates poor communication quality, which in turn indicates low AI model performance.
  • a smaller average latency indicates better communication quality, which in turn indicates high AI model performance.
  • the fourth parameter may be proportional to the performance of the AI model. For example, a larger fourth parameter in the statistical information and distribution information indicates a higher performance of the AI model, and a smaller fourth parameter in the statistical information and distribution information indicates a lower performance of the AI model.
  • the fourth parameter may be any parameter in the statistical information and distribution information that is proportional to the performance of the AI model, and this disclosure is not limited thereto.
  • the second target value is a target value corresponding to a parameter in the statistical information and/or distribution information of the measured CSI-related information.
  • the second target value may be a target value corresponding to the average delay; if the parameter in the statistical information and distribution information is delay spread, the second target value may be a target value corresponding to delay spread.
  • the third threshold, the fourth threshold, the second duration, the second target value, the threshold corresponding to the third parameter, and the threshold corresponding to the fourth parameter can be predefined by the protocol and/or configured by the network device, or can be determined based on any other feasible implementation method, and the embodiments of the present disclosure are not limited to this.
  • the terminal device may determine that a first event has been triggered.
  • the third parameter may be an average delay
  • the threshold corresponding to the average delay may be a preset delay. If the average delay calculated by the terminal device is greater than the preset delay, it indicates that the current communication quality is poor, and the terminal device triggers the first event, and may then send a performance monitoring request for the AI model to the network device.
  • the terminal device may determine that a first event has been triggered. For example, if the fourth parameter calculated by the terminal device is less than the threshold corresponding to the fourth parameter, it indicates that the current communication quality is poor, the terminal device triggers the first event, and then can send a performance monitoring request of the AI model to the network device.
  • the terminal device can determine that the first event is triggered.
  • the third parameter can be the average delay
  • the threshold corresponding to the average delay can be threshold 1
  • the third threshold can be threshold 2.
  • the terminal device can determine that the current communication quality is poor, the terminal device triggers the first event, and then can send a performance monitoring request for the AI model to the network device.
  • the second time length is 10 seconds
  • the threshold corresponding to the average delay is the preset delay
  • the third threshold is 3 times. If the average delay calculated by the terminal device within 10 seconds is greater than the preset delay 4 times, the terminal device can determine that the first event is triggered and send a performance monitoring request for the AI model to the network device.
  • the terminal device can determine that the first event is triggered.
  • the threshold corresponding to the fourth parameter can be threshold 1
  • the third threshold can be threshold 2.
  • the terminal device can determine that the current communication quality is poor, the terminal device triggers the first event, and then can send a performance monitoring request for the AI model to the network device.
  • the second time length is 10 seconds
  • the threshold corresponding to the fourth parameter is a preset threshold
  • the third threshold is 3 times. If the fourth parameter corresponding to the statistical information and/or distribution information of the CSI-related information measured by the terminal device within 10 seconds is less than the preset threshold four times, the terminal device can determine that the first event is triggered, and send a performance monitoring request for the AI model to the network device.
  • the terminal device can determine that the first event is triggered.
  • the parameter in the statistical information and/or distribution information of the measured CSI-related information may include a third parameter and a fourth parameter, wherein each parameter has a corresponding second target value. If the deviation of the parameter in the statistical information and/or distribution information of the measured CSI-related information from the second target value corresponding to the parameter is greater than the fourth threshold, it means that the current communication quality is poor, and the terminal device can send a performance monitoring request for the AI model to the network device.
  • the parameter in the statistical information and/or distribution information of the measured CSI-related information is the average delay
  • the second target value corresponding to the average delay is the preset delay. If the difference between the average delay calculated by the terminal device and the preset delay is greater than the fourth threshold, the terminal device can determine that the first event is triggered and send a performance monitoring request to the network device.
  • S903 Send a performance monitoring request to the network device.
  • the terminal device when the terminal device determines that the first event is currently triggered based on the statistical information and/or distribution information of the measured CSI-related information, the terminal device can send a performance monitoring request of the AI model to the network device.
  • the embodiments of the present disclosure provide a method for sending a performance monitoring request, obtaining statistical information and/or distribution information of measured CSI-related information, determining that a terminal device triggers a first event based on the statistical information and/or distribution information of the measured CSI-related information, and sending a performance monitoring request to a network device.
  • the terminal device can determine the communication quality based on the statistical information and/or distribution information of the measured CSI-related information, and determine whether to enable performance monitoring of the AI model based on the communication quality.
  • the terminal device can promptly monitor the performance of the AI model, thereby improving the performance monitoring accuracy of the AI model and thereby improving the stability of the communication.
  • FIG10 is a schematic diagram of another method for sending a performance monitoring request according to an embodiment of the present disclosure.
  • the first event is determined based on statistical information and/or distribution information of CSI-related information inferred by the AI model.
  • the method flow includes:
  • the statistical information and/or distribution information of the CSI-related information inferred by the AI model may include information such as average delay and delay spread corresponding to the CSI-related information inferred by the AI model.
  • the CSI-related information may be beam information
  • the statistical information and/or distribution information corresponding to the beam information inferred by the AI model may include average delay, delay spread, Doppler shift, Doppler spread, LOS distribution, NLOS distribution, channel covariance matrix, and KL (Kullback-Leibler) divergence, etc. associated with the beam information.
  • the terminal device can obtain statistical information and/or distribution information of CSI-related information inferred by the AI model according to any feasible implementation method, and the embodiments of the present disclosure are not limited to this.
  • the first event includes at least one of the following:
  • a fifth parameter in the statistical information and/or distribution information of the inferred CSI-related information is greater than a threshold corresponding to the fifth parameter
  • a sixth parameter in the statistical information and/or distribution information of the inferred CSI-related information is less than a threshold corresponding to the sixth parameter
  • the number of times that a fifth parameter in the statistical information and/or distribution information of the CSI-related information inferred within the third duration is greater than a threshold corresponding to the fifth parameter is greater than the fifth threshold;
  • the number of times that a sixth parameter in the statistical information and/or distribution information of the CSI-related information inferred within the third duration is greater than a threshold corresponding to the sixth parameter is greater than the fifth threshold;
  • a deviation between a parameter in the statistical information and/or distribution information of the inferred CSI-related information and the third target value is greater than a sixth threshold.
  • the fifth parameter may be inversely proportional to the performance of the AI model.
  • the fifth parameter may be average latency.
  • a larger average latency indicates poor communication quality, which in turn indicates low AI model performance.
  • a smaller average latency indicates better communication quality, which in turn indicates high AI model performance.
  • the sixth parameter may be proportional to the performance of the AI model.
  • a larger sixth parameter in the statistical information and distribution information indicates a higher performance of the AI model
  • a smaller sixth parameter in the statistical information and distribution information indicates a lower performance of the AI model.
  • the sixth parameter may be any parameter in the statistical information and distribution information that is proportional to the performance of the AI model, and this disclosure is not limited thereto.
  • the third target value is a target value corresponding to a parameter in the statistical information and/or distribution information of the inferred CSI-related information.
  • the third target value may be a target value corresponding to the average delay; if the parameter in the statistical information and distribution information is delay spread, the third target value may be a target value corresponding to delay spread.
  • the fifth threshold, the sixth threshold, the third time duration, the third target value, the threshold corresponding to the fifth parameter, and the threshold corresponding to the sixth parameter can be predefined by the protocol and/or configured by the network device, or can be determined based on any other feasible implementation method, and the embodiments of the present disclosure are not limited to this.
  • the terminal device may determine that a first event has been triggered.
  • the fifth parameter may be average latency
  • the threshold corresponding to the average latency may be a preset latency. If the average latency calculated by the terminal device is greater than the preset latency, it indicates that the current communication quality is poor, and the terminal device triggers the first event, thereby sending a performance monitoring request for the AI model to the network device.
  • the terminal device may determine that a first event has been triggered. For example, if the sixth parameter calculated by the terminal device is less than the threshold corresponding to the sixth parameter, it indicates that the current communication quality is poor, and the terminal device triggers the first event, and then may send a performance monitoring request of the AI model to the network device.
  • the terminal device can determine that the first event is triggered.
  • the fifth parameter can be the average delay
  • the threshold corresponding to the average delay can be threshold 1
  • the fifth threshold can be threshold 2.
  • the terminal device can determine that the current communication quality is poor, the terminal device triggers the first event, and then can send a performance monitoring request for the AI model to the network device.
  • the third time length is 10 seconds
  • the threshold corresponding to the average delay is the preset delay
  • the fifth threshold is 3 times. If the average delay calculated by the terminal device within 10 seconds is greater than the preset delay four times, the terminal device can determine that the first event is triggered and send a performance monitoring request for the AI model to the network device.
  • the terminal device can determine that the first event is triggered.
  • the threshold corresponding to the sixth parameter can be threshold 1
  • the fifth threshold can be threshold 2.
  • the terminal device can determine that the current communication quality is poor, the terminal device triggers the first event, and then can send a performance monitoring request for the AI model to the network device.
  • the third time length is 10 seconds
  • the threshold corresponding to the sixth parameter is a preset threshold
  • the fifth threshold is 3 times. If the sixth parameter calculated by the terminal device within 10 seconds is less than the preset threshold four times, the terminal device can determine that the first event is triggered and send a performance monitoring request for the AI model to the network device.
  • the terminal device can determine that the first event is triggered.
  • the parameters in the statistical information and/or distribution information of the inferred CSI-related information may include a fifth parameter and a sixth parameter, wherein each parameter has a corresponding third target value. If the deviation of the parameters in the statistical information and/or distribution information of the inferred CSI-related information from the third target value corresponding to the parameter is greater than the sixth threshold, it means that the current communication quality is poor, and the terminal device can send a performance monitoring request for the AI model to the network device.
  • the parameter in the statistical information of the inferred CSI-related information is the average delay
  • the third target value corresponding to the average delay is the preset delay. If the difference between the average delay calculated by the terminal device and the preset delay is greater than the sixth threshold, the terminal device can determine that the first event is triggered and send a performance monitoring request to the network device.
  • the terminal device when the terminal device determines that the first event is currently triggered based on the statistical information and/or distribution information of the inferred CSI-related information, the terminal device can send a performance monitoring request of the AI model to the network device.
  • the embodiments of the present disclosure provide a method for sending a performance monitoring request, obtaining statistical information and/or distribution information of inferred CSI-related information, determining that a terminal device triggers a first event based on the statistical information and/or distribution information of the inferred CSI-related information, and sending a performance monitoring request to a network device.
  • the terminal device can determine the communication quality based on the statistical information and/or distribution information of the inferred CSI-related information, and determine whether to enable performance monitoring of the AI model based on the communication quality.
  • the terminal device can promptly monitor the performance of the AI model, thereby improving the performance monitoring accuracy of the AI model and thereby improving the stability of the communication.
  • FIG11 is a schematic diagram of another method for sending a performance monitoring request according to an embodiment of the present disclosure.
  • the first event is an event determined based on the generalization indicator of the AI model, and the method flow includes:
  • the generalization indicators of the AI model can indicate the performance of the AI model.
  • the generalization indicators of the AI model can include the environment, scene, speed, location, physical layer carrier resource configuration, etc. of the terminal device (where the AI model is located).
  • the terminal device can obtain the generalization index of the AI model according to any feasible implementation method, and the embodiments of the present disclosure are not limited to this.
  • S1102. Determine triggering of a first event based on a generalization indicator of the AI model.
  • the first event is an event determined based on the generalization indicator of the AI model, the first event includes at least one of the following:
  • a duration during which a parameter in a performance indicator of the received reference signal is less than a seventh threshold and greater than an eighth threshold
  • the quasi co-location information of the received reference signal changes
  • the duration during which the parameter in the measured speed information is greater than the tenth threshold is greater than the eleventh threshold
  • the number of times that the parameter in the measured speed information is greater than the tenth threshold within the fifth time period is greater than the twelfth threshold
  • the distance difference between the two positions measured at a sixth time interval is greater than the thirteenth threshold.
  • the reference signal may be a signal containing information related to CSI processed by the AI model.
  • the reference signal may be a CSI-related reference signal
  • the reference signal may be a beam-related reference signal
  • the reference signal may be a positioning-related reference signal.
  • the performance indicators of the reference signal may include SINR, L1 (L1 level filtering)-RSRP, L1-RSRQ, and L3 (L3 level filtering)-RSRP.
  • the performance indicators of the reference signal may also include any other indicators, which are not limited in the embodiments of the present disclosure.
  • the terminal device may receive the reference signal sent by the network device.
  • Quasi Co-Location (QCL) information can include QCL source and QCL type.
  • the fourth time duration, the fifth time duration, the sixth time duration, the seventh threshold, the eighth threshold, the ninth threshold, the tenth threshold, the eleventh threshold, the twelfth threshold, and the thirteenth threshold can be predefined by the protocol and/or configured by the network device, or can be determined based on any other feasible implementation method, and the embodiments of the present disclosure are not limited to this.
  • the terminal device may determine that a first event has been triggered.
  • the performance indicator of the reference signal may be a SINR. If the duration of the SINR being less than the seventh threshold for a duration greater than the eighth threshold indicates poor communication quality, the terminal device may determine that the first event has been triggered and send a performance monitoring request to the network device.
  • the terminal device can determine that the first event is triggered.
  • the performance indicator of the reference signal can be SINR. If, within the fourth time length, the number of times the SINR determined by the terminal device is less than the seventh threshold is greater than the ninth threshold, it means that the current communication quality is poor, and the terminal device can determine that the first event is triggered, and send a performance monitoring request to the network device.
  • the fourth time length is 10 seconds
  • the ninth threshold is 3 times. Within 10 seconds, if the number of times the SINR determined by the terminal device is less than the seventh threshold is 5 times, the terminal device can determine that the first event is triggered, and send a performance monitoring request to the network device.
  • the terminal device may determine that a first event has been triggered. For example, if the QCL source of the reference signal received by the terminal device changes, the terminal device may determine that the first event has been triggered and send a performance monitoring request to the network device. For example, if the QCL type of the reference signal received by the terminal device changes, the terminal device may determine that the first event has been triggered and send a performance monitoring request to the network device.
  • the terminal device may determine that a first event has been triggered.
  • the dataset tag may indicate the environment and scenario of the terminal device.
  • the terminal device may determine whether the terminal device is located indoors or outdoors. For example, if the terminal device moves from indoors to outdoors, the dataset tag changes, and the terminal device may determine that the first event has been triggered and send a performance monitoring request to the network device.
  • the terminal device can determine that the first event is triggered.
  • the parameter in the speed information can be the absolute value of the line speed. If the absolute value of the line speed measured by the terminal device is greater than the tenth threshold for a duration greater than the eleventh threshold, it means that the terminal device is moving quickly. The terminal device can determine that the first event is triggered and send a performance monitoring request for the model to the network device. For example, the tenth threshold is 10 meters per second and the eleventh threshold is 60 seconds. If the absolute value of the line speed measured by the terminal device is 15 meters per second within 100 seconds, the terminal device can determine that the first event is triggered and send a performance monitoring request for the model to the network device.
  • the terminal device can determine that the first event is triggered.
  • the parameter in the speed information can be the absolute value of the line speed.
  • the absolute value of the line speed measured by the terminal device is greater than the tenth threshold number of times and greater than the twelfth threshold, it means that the terminal device is moving quickly.
  • the terminal device can determine that the first event is triggered and send a performance monitoring request for the model to the network device.
  • the fifth time period is 10 seconds
  • the tenth threshold is 10 meters/second
  • the twelfth threshold is 5 times. If within 10 seconds, the absolute value of the line speed measured by the terminal device is greater than 10 meters/second 10 times, the terminal device can determine that the first event is triggered and send a performance monitoring request for the model to the network device.
  • the terminal device can determine that the first event is triggered. For example, the terminal device measures the position at time 1 to obtain position 1, and the terminal device measures the position at time 2 to obtain position 2 (the time duration between time 1 and time 2 is the sixth time duration). If the distance difference between position 1 and position 2 is greater than the thirteenth threshold, the terminal device can determine that the first event is triggered and send a performance monitoring request to the network device.
  • the terminal device can determine that the first event is triggered and send a performance monitoring request to the network device.
  • the terminal device may determine that the first event is triggered. For example, if the operating center frequency of the terminal device changes, the terminal device may determine that the first event is triggered; if the subcarrier of the terminal device changes, the terminal device may determine that the first event is triggered; if the bandwidth configuration of the terminal device changes, the terminal device may determine that the first event is triggered.
  • the terminal device when the terminal device determines that the first event is currently triggered based on the generalization indicator of the AI model, the terminal device can send a performance monitoring request of the AI model to the network device.
  • the disclosed embodiments provide a method for sending a performance monitoring request, obtaining a generalization index of an AI model, determining, based on the generalization index of the AI model, that a terminal device has triggered a first event, and sending a performance monitoring request to a network device.
  • the terminal device can determine, based on the generalization index of the AI model, that the terminal device's network status has changed, thereby enabling timely performance monitoring of the AI model, improving the accuracy of AI model performance monitoring, saving communication resources, and thereby improving communication stability.
  • FIG12 is a schematic diagram of another method for sending a performance monitoring request according to an embodiment of the present disclosure.
  • the first event is an event determined based on AI model update information and/or model transfer information.
  • the method flow includes:
  • S1201 Receive model transfer information and/or model update information sent by a network device.
  • the AI model update information may indicate an AI model update
  • the AI model transfer information may indicate an AI model transfer.
  • a terminal device may receive AI model update information and AI model transfer information sent by a network device or server, and the terminal device may perform lifecycle management operations on the AI model based on the above information.
  • the terminal device can obtain the model transmission information or model update information of the AI model according to any feasible implementation method, and the embodiments of the present disclosure are not limited to this.
  • S1202 Send a performance monitoring request to the network device.
  • the first event is an event determined based on AI model update information or model transfer information
  • the first event includes at least one of the following:
  • no performance monitoring related indication is received within a seventh period of time.
  • the seventh duration may be predefined by the protocol and/or configured by the network device, or may be determined based on any other feasible implementation method, and the embodiments of the present disclosure are not limited to this.
  • the terminal device can also receive the model update information and model transfer information sent by the server, which is not limited in the embodiments of the present disclosure.
  • the terminal device can determine that the first event is triggered. For example, if the terminal device receives model update information sent by the network device, it means that the terminal device can perform lifecycle management operations on the AI model. Therefore, the terminal device can send a performance monitoring request for the AI model to the network device, and then monitor the performance of the updated AI model. For example, if the terminal device receives model transfer information sent by the network device, it means that the terminal device can perform lifecycle management operations on the AI model. Therefore, the terminal device can send a performance monitoring request for the AI model to the network device, and then monitor the performance of the transferred AI model.
  • the terminal device may determine that the first event is triggered. For example, after the terminal device receives the model update information and/or model transfer information sent by the network device, the terminal device may monitor the performance of the updated AI model or the transferred AI model. If the terminal device does not receive an indication related to performance monitoring sent by the network device, the terminal device may determine that the first event is triggered and send a performance monitoring request to the network device.
  • the disclosed embodiments provide a method for sending a performance monitoring request, which receives model transfer information and/or model update information sent by a network device and sends a performance monitoring request to the network device.
  • the terminal device can start monitoring the performance of the AI model, thereby improving the accuracy of performance monitoring and enhancing communication quality.
  • the triggering event includes a second event related to the performance of the terminal device, wherein the second event can be an event determined based on the upper layer signaling of the terminal device.
  • FIG13 is a schematic diagram of a method for sending a performance monitoring request according to an embodiment of the present disclosure. Referring to FIG13 , the method flow includes:
  • the upper-layer signaling may include any information in the terminal device that interacts with the upper-layer protocol layer, which is not limited in the embodiments of the present disclosure.
  • the terminal device can obtain the upper-layer signaling according to any feasible implementation method, which is not limited in the embodiments of the present disclosure.
  • S1302 Determine, according to upper layer signaling, whether to trigger a second event.
  • the second event is an event determined based on upper layer signaling, the second event includes at least one of the following:
  • a handover occurs in the cell where the cell is located
  • the first monitoring quantity is greater than the fourteenth threshold
  • the second monitoring value is less than the fifteenth threshold
  • a first parameter of a final key performance indicator at a future moment predicted based on the final key performance indicator at the current moment is less than a threshold corresponding to the first parameter
  • a second parameter of the final key performance indicator at a future moment predicted based on the final key performance indicator at the current moment is greater than a threshold corresponding to the second parameter.
  • the first monitored quantity is inversely proportional to the performance of the terminal device.
  • the first monitored quantity may include power consumption, memory usage, etc. of the terminal device, which is not limited in the present embodiment.
  • the second monitored quantity is proportional to the performance of the terminal device.
  • the second monitored quantity may include the remaining computing power of the terminal device, the remaining memory of the terminal device, etc., which is not limited in the present embodiment.
  • the fourteenth threshold and the fifteenth threshold may be predefined by the protocol and/or configured by the network device, or may be determined based on any other feasible implementation manner, and the embodiments of the present disclosure do not limit this.
  • the terminal device may determine that a second event is triggered. For example, when a handover occurs in the cell where the terminal device is located, the terminal device needs to determine the currently available AI model. Therefore, the terminal device may determine that a second event is triggered and send a monitoring request to the network device. For example, the terminal device may send a monitoring request to the network device based on the cell handover trigger of the terminal device. The cell handover measurement time of the terminal device and the cell handover signaling may trigger the terminal device to send a monitoring request for the AI model.
  • the terminal device may determine that a second event has been triggered.
  • the first monitored quantity may be the power consumption of the terminal device. If the power consumption of the terminal device is greater than the fourteenth threshold, it indicates that the performance of the terminal device may affect the communication quality.
  • the terminal device may determine that a second event has been triggered and send a monitoring request to the network device.
  • the first monitored quantity may be power consumption
  • the fourteenth threshold may be a preset power consumption. If the current power consumption obtained by the terminal device is greater than the preset power consumption, it indicates that the current performance of the terminal device is poor, which in turn may affect the communication quality of the terminal device. Therefore, the terminal device may determine that a second event has been triggered.
  • the terminal device may determine that a second event has been triggered.
  • the second monitored quantity may be the remaining computing power of the terminal device. If the remaining computing power of the terminal device is less than the fourteenth threshold, it indicates that the performance of the terminal device will affect the communication quality.
  • the terminal device may determine that the second event has been triggered and send a monitoring request to the network device.
  • the second monitored quantity may be the remaining computing power
  • the fifteenth threshold is the preset computing power. If the current remaining computing power obtained by the terminal device is less than the preset computing power, it indicates that the current performance of the terminal device is poor, which in turn will affect the communication quality of the terminal device. Therefore, the terminal device may determine that the second event has been triggered.
  • the terminal device may determine that a second event has been triggered.
  • the first parameter may be throughput. If the throughput of the final KPI at a future time predicted by the terminal device based on the current throughput is less than a threshold corresponding to the throughput, the terminal device may determine that a second event has been triggered.
  • the terminal device may determine that a second event has been triggered.
  • the second parameter may be a block error rate. If the block error rate of the final KPI at a future time predicted by the terminal device based on the current throughput is greater than a threshold corresponding to the block error rate, the terminal device may determine that the second event has been triggered.
  • the terminal device when the terminal device determines that the second event is currently triggered based on the upper layer signaling, the terminal device can send a performance monitoring request of the AI model to the network device.
  • the disclosed embodiments provide a method for sending a performance monitoring request, which obtains upper-layer signaling, determines based on the upper-layer signaling that a terminal device has triggered a second event, and then sends a performance monitoring request to a network device.
  • the terminal device can determine, based on the upper-layer signaling, that the second event is related to the terminal device's performance. Because this second event can affect the terminal device's communications, when the terminal device triggers the second event, the terminal device can promptly send a performance monitoring request for the model to the network device, thereby improving the timeliness and accuracy of performance monitoring.
  • FIG14 is a schematic diagram of a method for sending a response to a performance monitoring request to a terminal device according to an embodiment of the present disclosure.
  • the method flow includes:
  • the network device receives a performance monitoring request for the AI model sent by the terminal device.
  • the monitoring request may be determined by the terminal device based on a triggering event.
  • triggering event and the information carried in the monitoring request can refer to the embodiments shown in Figures 2 to 13, and the embodiments of the present disclosure will not be described in detail here.
  • the network device sends a response to the performance monitoring request to the terminal device.
  • the response of the network device to the performance monitoring request sent to the terminal device can be: judging whether the AI model needs performance monitoring based on the information carried in the performance monitoring request, obtaining a judgment result, and sending a response to the performance monitoring request to the terminal device based on the judgment result.
  • a network device When a network device receives a performance monitoring request, it can determine whether to perform performance monitoring on the AI model based on the target information in the performance monitoring request. For example, if the information carried in the performance monitoring request indicates a high throughput, the network device may refuse to perform performance monitoring on the AI model. If the information carried in the monitoring request indicates a low throughput, the network device may agree to perform performance monitoring on the AI model.
  • the response of the performance monitoring request sent by the network device to the terminal device may be agreement; if the judgment result is refusal to perform performance monitoring on the AI model, the response of the performance monitoring request sent by the network device to the terminal device may be refusal.
  • the network device may send a response to the performance monitoring request to the terminal device, and the response may be an agreement to start performance monitoring or an agreement to activate performance monitoring.
  • the disclosed embodiments provide a method for sending a response to a performance monitoring request to a terminal device.
  • a network device receives a performance monitoring request for an AI model sent by the terminal device, determines whether the AI model requires performance monitoring based on the information contained in the performance monitoring request, obtains a determination result, and sends a response to the performance monitoring request to the terminal device based on the determination result.
  • the network device can accurately determine whether to enable performance monitoring of the AI model based on the target information reported by the terminal device, thereby improving the accuracy of the AI model's performance monitoring.
  • FIG15 is a schematic diagram of the structure of a performance monitoring device provided by an embodiment of the present disclosure.
  • the performance monitoring device 1500 includes a sending module 1501, a receiving module 1502, and a monitoring module 1503, wherein:
  • the sending module 1501 is used to send a performance monitoring request of the AI model to the network device according to the triggering event;
  • the receiving module 1502 is configured to receive a response to the performance monitoring request sent by the network device;
  • the monitoring module 1503 is used to monitor the performance of the AI model based on the response to the performance monitoring request.
  • the performance monitoring request includes an identification of the AI model and/or an identification of a function.
  • the performance monitoring request includes an identifier of an applicable model, an identifier of an applicable function, and an identifier of an applicable scenario corresponding to the performance monitoring request.
  • the performance monitoring request includes a start time of performance monitoring and/or an end time of performance monitoring.
  • the performance monitoring request includes target information, which is related to the performance indicators of the AI model and/or the measurement values of the AI model.
  • the target information includes at least one of the following:
  • CSI channel state information
  • the reference signal received power associated with the AI model
  • the trigger event includes a first event related to the AI model and/or function and a second event related to the performance of the terminal device.
  • the first event when the AI model is not activated, not selected, or not paired, the first event includes at least one of the following:
  • the first event is an event determined based on at least one of the following information:
  • the update information of the AI model and/or the transfer information of the AI model are provided.
  • the first event is an event determined based on a final key performance indicator of the measurement, and the first event includes at least one of the following:
  • a first parameter in the final key performance indicator is less than a threshold corresponding to the first parameter, and the first parameter is proportional to the performance of the AI model;
  • a second parameter in the final key performance indicator is greater than a threshold corresponding to the second parameter, and the second parameter is inversely proportional to the performance of the AI model;
  • the number of times that the first parameter in the final key performance indicator is less than the threshold corresponding to the first parameter within the first time period is greater than the first threshold
  • the number of times that the second parameter in the final key performance indicator is greater than the threshold corresponding to the second parameter within the first time period is greater than the first threshold
  • a deviation between the parameter in the final key performance indicator and a first target value is greater than a second threshold, and the first target value is a target value corresponding to the parameter in the final key performance indicator.
  • the first event is an event determined based on statistical information and/or distribution information of measured CSI-related information, and the first event includes at least one of the following:
  • a third parameter in the statistical information and/or distribution information of the measured CSI-related information is greater than a threshold corresponding to the third parameter, and the third parameter is inversely proportional to the performance of the AI model;
  • a fourth parameter in the statistical information and/or distribution information of the measured CSI-related information is less than a threshold corresponding to the fourth parameter, and the fourth parameter is proportional to the performance of the AI model;
  • the number of times that a third parameter in the statistical information and/or distribution information of the measured CSI-related information within the second duration is greater than a threshold corresponding to the third parameter is greater than the third threshold;
  • the number of times that a fourth parameter in the statistical information and/or distribution information of the measured CSI-related information within the second duration is less than a threshold corresponding to the fourth parameter is greater than the third threshold
  • a deviation between the parameter in the statistical information and/or distribution information of the measured CSI-related information and the second target value is greater than a fourth threshold, and the second target value is a target value corresponding to the parameter in the statistical information and/or distribution information of the measured CSI-related information.
  • the first event is an event determined based on statistical information and/or distribution information of CSI-related information inferred by the AI model, and the first event includes at least one of the following:
  • a fifth parameter in the statistical information and/or distribution information of the inferred CSI-related information is greater than a threshold corresponding to the fifth parameter, and the fifth parameter is inversely proportional to the performance of the AI model;
  • a sixth parameter in the statistical information and/or distribution information of the inferred CSI-related information is less than a threshold corresponding to the sixth parameter, and the sixth parameter is proportional to the performance of the AI model;
  • the number of times that a fifth parameter in the statistical information and/or distribution information of the inferred CSI-related information within the third duration is greater than a threshold corresponding to the fifth parameter is greater than the fifth threshold;
  • a deviation between the parameters in the statistical information and/or distribution information of the inferred CSI-related information and the third target value is greater than a sixth threshold, and the third target value is a target value corresponding to the parameters in the statistical information and/or distribution information of the inferred CSI-related information.
  • the first event is an event determined based on a generalization indicator of the AI model, and the first event includes at least one of the following:
  • a duration during which a parameter in a performance indicator of the received reference signal is less than a seventh threshold and greater than an eighth threshold
  • the number of times that the parameter in the performance indicator of the received reference signal is less than the seventh threshold within the fourth time duration is greater than the ninth threshold
  • the quasi co-location information of the received reference signal changes
  • the duration during which the parameter in the measured speed information is greater than the tenth threshold is greater than the eleventh threshold
  • the number of times that the parameter in the measured speed information is greater than the tenth threshold within the fifth time period is greater than the twelfth threshold
  • the distance difference between the two positions measured at the sixth time interval is greater than the thirteenth threshold
  • the operating center frequency, subcarrier and/or bandwidth configuration changes.
  • the first event is an event determined based on update information and/or model transfer information of the AI model, and the first event includes at least one of the following:
  • no performance monitoring related indication is received within a seventh time period.
  • the second event is an event determined based on upper layer signaling, and the second event includes at least one of the following:
  • a handover occurs in the cell where the cell is located
  • a first monitoring quantity is greater than a fourteenth threshold, and the first monitoring quantity is inversely proportional to the performance of the terminal device;
  • the second monitoring amount is less than a fifteenth threshold, and the first monitoring amount is proportional to the performance of the terminal device;
  • a first parameter of a final key performance indicator at a future moment predicted based on the final key performance indicator at the current moment is less than a threshold corresponding to the first parameter
  • a second parameter of the final key performance indicator at a future moment predicted based on the final key performance indicator at the current moment is greater than a threshold corresponding to the second parameter.
  • the threshold, duration, and target value are based on protocol pre-definition and/or network-side configuration and/or a mixture of protocol pre-definition and network-side configuration.
  • the CSI-related information includes the CSI information, beam information and positioning information.
  • the response to the performance monitoring request includes a reply or indication of the performance monitoring request.
  • the sending module 1501 is used to:
  • the performance monitoring request is sent to the network device based on the physical uplink control channel PUCCH resources or the physical uplink shared channel PUSCH resources of the reserved period.
  • FIG16 is a schematic diagram of the structure of another performance monitoring device provided by an embodiment of the present disclosure.
  • the performance monitoring device 1600 includes a receiving module 1601 and a sending module 1602, wherein:
  • the receiving module 1601 is configured to receive a performance monitoring request for an AI model sent by a terminal device, where the performance monitoring request is determined by the terminal device based on a triggering event;
  • the sending module 1602 is configured to send a response to the performance monitoring request to the terminal device.
  • the sending module 1602 is configured to:
  • a response to the monitoring request is sent to the terminal device.
  • the sending module 1602 is configured to:
  • a response to the performance monitoring request is sent to the terminal device.
  • the sending module is configured to:
  • a response to the scheduling request SR is sent to the terminal device, where the response to the scheduling request SR includes downlink control information DCI for uplink transmission resource allocation, and the downlink control information DCI is encrypted based on the radio network temporary identifier RNTI of the terminal device.
  • the division of units in the embodiments of the present disclosure is schematic and is merely a logical functional division. In actual implementation, other division methods may be used.
  • the functional units in the various embodiments of the present disclosure may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit.
  • the aforementioned integrated units may be implemented in the form of hardware or software functional units.
  • the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a processor-readable storage medium.
  • the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the various embodiments of the present disclosure.
  • the aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and other media that can store program codes.
  • FIG17 is a schematic diagram of the structure of a terminal device provided by an embodiment of the present disclosure.
  • the terminal device includes a memory 1710, a transceiver 1720, and a processor 1730:
  • the memory 1710 is used to store computer programs
  • the transceiver 1720 is configured to transmit and receive data under the control of the processor
  • the processor 1730 is configured to read the computer program in the memory and perform the following operations:
  • the performance monitoring request includes an identification of the AI model and/or an identification of a function.
  • the performance monitoring request includes an identifier of an applicable model, an identifier of an applicable function, and an identifier of an applicable scenario corresponding to the performance monitoring request.
  • the performance monitoring request includes a start time of performance monitoring and/or an end time of performance monitoring.
  • the performance monitoring request includes target information, which is related to the performance indicators of the AI model and/or the measurement values of the AI model.
  • the target information includes at least one of the following:
  • CSI channel state information
  • the reference signal received power associated with the AI model
  • the trigger event includes a first event related to the AI model and/or function and a second event related to the performance of the terminal device.
  • the first event when the AI model is not activated, not selected, or not paired, the first event includes at least one of the following:
  • the first event is an event determined based on at least one of the following information:
  • the update information of the AI model and/or the transfer information of the AI model are provided.
  • the first event is an event determined based on a final key performance indicator of the measurement, and the first event includes at least one of the following:
  • a first parameter in the final key performance indicator is less than a threshold corresponding to the first parameter, and the first parameter is proportional to the performance of the AI model;
  • a second parameter in the final key performance indicator is greater than a threshold corresponding to the second parameter, and the second parameter is inversely proportional to the performance of the AI model;
  • the number of times that the first parameter in the final key performance indicator is less than the threshold corresponding to the first parameter within the first time period is greater than the first threshold
  • the number of times that the second parameter in the final key performance indicator is greater than the threshold corresponding to the second parameter within the first duration is greater than the first threshold
  • a deviation between the parameter in the final key performance indicator and a first target value is greater than a second threshold, and the first target value is a target value corresponding to the parameter in the final key performance indicator.
  • the first event is an event determined based on statistical information and/or distribution information of measured CSI-related information, and the first event includes at least one of the following:
  • a third parameter in the statistical information and/or distribution information of the measured CSI-related information is greater than a threshold corresponding to the third parameter, and the third parameter is inversely proportional to the performance of the AI model;
  • a fourth parameter in the statistical information and/or distribution information of the measured CSI-related information is less than a threshold corresponding to the fourth parameter, and the fourth parameter is proportional to the performance of the AI model;
  • the number of times that a third parameter in the statistical information and/or distribution information of the measured CSI-related information within the second duration is greater than a threshold corresponding to the third parameter is greater than the third threshold;
  • the number of times that a fourth parameter in the statistical information and/or distribution information of the measured CSI-related information within the second duration is less than a threshold corresponding to the fourth parameter and greater than the third threshold;
  • a deviation between the parameter in the statistical information and/or distribution information of the measured CSI-related information and the second target value is greater than a fourth threshold, and the second target value is a target value corresponding to the parameter in the statistical information and/or distribution information of the measured CSI-related information.
  • the first event is an event determined based on statistical information and/or distribution information of CSI-related information inferred by the AI model, and the first event includes at least one of the following:
  • a fifth parameter in the statistical information and/or distribution information of the inferred CSI-related information is greater than a threshold corresponding to the fifth parameter, and the fifth parameter is inversely proportional to the performance of the AI model;
  • a sixth parameter in the statistical information and/or distribution information of the inferred CSI-related information is less than a threshold corresponding to the sixth parameter, and the sixth parameter is proportional to the performance of the AI model;
  • a deviation between the parameters in the statistical information and/or distribution information of the inferred CSI-related information and the third target value is greater than a sixth threshold, and the third target value is a target value corresponding to the parameters in the statistical information and/or distribution information of the inferred CSI-related information.
  • the first event is an event determined based on a generalization indicator of the AI model, and the first event includes at least one of the following:
  • a duration during which a parameter in a performance indicator of a received reference signal is less than a seventh threshold and greater than an eighth threshold
  • the number of times that the parameter in the performance indicator of the received reference signal is less than the seventh threshold within the fourth time duration is greater than the ninth threshold
  • the quasi co-location information of the received reference signal changes
  • the duration during which the parameter in the measured speed information is greater than the tenth threshold is greater than the eleventh threshold
  • the number of times that the parameter in the measured speed information is greater than the tenth threshold within the fifth time period is greater than the twelfth threshold
  • the distance difference between the two positions measured at the sixth time interval is greater than the thirteenth threshold
  • the operating center frequency, subcarrier and/or bandwidth configuration changes.
  • the first event is an event determined based on update information and/or model transfer information of the AI model, and the first event includes at least one of the following:
  • no performance monitoring related indication is received within a seventh time period.
  • the second event is an event determined based on upper layer signaling, and the second event includes at least one of the following:
  • a handover occurs in the cell where the cell is located
  • a first monitoring quantity is greater than a fourteenth threshold, and the first monitoring quantity is inversely proportional to the performance of the terminal device;
  • the second monitoring amount is less than a fifteenth threshold, and the first monitoring amount is proportional to the performance of the terminal device;
  • a first parameter of a final key performance indicator at a future moment predicted based on the final key performance indicator at the current moment is less than a threshold corresponding to the first parameter
  • a second parameter of the final key performance indicator at a future moment predicted based on the final key performance indicator at the current moment is greater than a threshold corresponding to the second parameter.
  • the threshold, duration, and target value are based on protocol pre-definition and/or network-side configuration and/or a mixture of protocol pre-definition and network-side configuration.
  • the CSI-related information includes the CSI information, beam information and positioning information.
  • the response to the performance monitoring request includes a reply or indication of the performance monitoring request.
  • sending a performance monitoring request of an AI model to a network device includes:
  • the performance monitoring request is sent to the network device based on the physical uplink control channel PUCCH resources or the physical uplink shared channel PUSCH resources of the reserved period.
  • the terminal device may further include a user interface 1740.
  • the user interface 1740 may also be an interface capable of connecting external or internal devices as required.
  • the connected devices include but are not limited to a keypad, display, speaker, microphone, joystick, etc.
  • the bus architecture may include any number of interconnected buses and bridges, linking together various circuits of one or more processors represented by processor 1703 and memory represented by memory 1710.
  • the bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein.
  • the bus interface provides an interface.
  • the transceiver 1720 may be a plurality of components, including a transmitter and a receiver, providing a unit for communicating with various other devices over a transmission medium, such as a wireless channel, a wired channel, an optical cable, and the like.
  • the processor 1730 is responsible for managing the bus architecture and general processing, and the memory 1701 may store data used by the processor 1730 when performing operations.
  • processor 1730 can be a central processing unit (CPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or a complex programmable logic device (CPLD), and the processor can also adopt a multi-core architecture.
  • CPU central processing unit
  • ASIC application specific integrated circuit
  • FPGA field programmable gate array
  • CPLD complex programmable logic device
  • the processor 1730 is configured to execute any of the methods provided by the embodiments of the present disclosure according to the obtained executable instructions by calling the computer program stored in the memory 1710.
  • the processor 1730 and the memory 1710 may also be physically separated.
  • FIG18 is a schematic diagram of the structure of a network device provided by an embodiment of the present disclosure.
  • the network device includes a memory 1810, a transceiver 1820, and a processor 1830:
  • the memory 1810 is used to store computer programs
  • the transceiver 1820 is configured to transmit and receive data under the control of the processor
  • the processor 1830 is configured to read the computer program in the memory and perform the following operations:
  • the sending of the response to the performance monitoring request to the terminal device includes:
  • a response to the performance monitoring request is sent to the terminal device.
  • sending a response to the performance monitoring request to the terminal device includes:
  • a response to the scheduling request SR is sent to the terminal device, where the response to the scheduling request SR includes downlink control information DCI for uplink transmission resource allocation, and the downlink control information DCI is encrypted based on the radio network temporary identifier RNTI of the terminal device.
  • the bus architecture may include any number of interconnected buses and bridges, linking together various circuits of one or more processors represented by processor 1830 and memory represented by memory 1810.
  • the bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein.
  • the bus interface provides an interface.
  • the transceiver 1820 may be a plurality of components, i.e., a transmitter and a receiver, providing a unit for communicating with various other devices over a transmission medium, such as a wireless channel, a wired channel, an optical cable, or the like.
  • the processor 1830 is responsible for managing the bus architecture and general processing, and the memory 1810 may store data used by the processor 1830 when performing operations.
  • processor 1830 can be a central processing unit (CPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or a complex programmable logic device (CPLD), and the processor can also adopt a multi-core architecture.
  • CPU central processing unit
  • ASIC application specific integrated circuit
  • FPGA field programmable gate array
  • CPLD complex programmable logic device
  • An embodiment of the present disclosure further provides a processor-readable storage medium, wherein the processor-readable storage medium stores a computer program, and the computer program is used to enable a processor to execute the method described in any one of the above method embodiments.
  • the processor-readable storage medium can be any available medium or data storage device that can be accessed by the computer, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CDs, DVDs, BDs, HVDs, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drives (SSDs)), etc.
  • magnetic storage such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.
  • optical storage such as CDs, DVDs, BDs, HVDs, etc.
  • semiconductor storage such as ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid-state drives (SSDs)
  • the embodiments of the present disclosure further provide a computer program product, including a computer program, which implements the method described in any one of the above method embodiments when the computer program is executed by a processor.
  • the embodiments of the present disclosure may be provided as methods, systems, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage, etc.) containing computer-usable program code.
  • a computer-usable storage media including but not limited to magnetic disk storage and optical storage, etc.
  • These computer-executable instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.
  • processor-executable instructions may also be stored in a processor-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the processor-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.
  • processor-executable instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and/or one or more boxes in the block diagram.

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Abstract

La présente divulgation se rapporte au domaine technique des communications, et concerne un procédé et un appareil de surveillance de performance, un dispositif et un support de stockage, appliqués à un dispositif terminal. Le procédé consiste à : envoyer une demande de surveillance de performance d'un modèle d'IA à un dispositif de réseau sur la base d'un événement de déclenchement ; et recevoir une réponse à la demande de surveillance de performance envoyée par le dispositif de réseau, effectuer une surveillance de performance sur le modèle d'IA sur la base de la réponse à la demande de surveillance de performance. Des ressources de communication pour la surveillance de modèle sont sauvegardées.
PCT/CN2024/142031 2024-01-25 2024-12-24 Procédé et appareil de surveillance de performance, dispositif et support de stockage Pending WO2025156910A1 (fr)

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CN202410108832.9A CN120378923A (zh) 2024-01-25 2024-01-25 性能监控方法、装置、设备及存储介质

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023151454A1 (fr) * 2022-02-14 2023-08-17 大唐移动通信设备有限公司 Procédé de contrôle de modèle, extrémité de contrôle, dispositif et support de stockage
CN116846763A (zh) * 2023-07-07 2023-10-03 北京佰才邦技术股份有限公司 模型监控的交互方法和系统、通信装置
WO2023187684A1 (fr) * 2022-03-29 2023-10-05 Telefonaktiebolaget Lm Ericsson (Publ) Détection d'erreurs assistée par le réseau pour l'intelligence artificielle sur une interface radio
WO2023240546A1 (fr) * 2022-06-16 2023-12-21 北京小米移动软件有限公司 Procédés et appareil de surveillance de modèle, dispositif et support
CN117378237A (zh) * 2023-07-10 2024-01-09 北京小米移动软件有限公司 通信方法、终端、网络设备以及通信系统

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2023151454A1 (fr) * 2022-02-14 2023-08-17 大唐移动通信设备有限公司 Procédé de contrôle de modèle, extrémité de contrôle, dispositif et support de stockage
WO2023187684A1 (fr) * 2022-03-29 2023-10-05 Telefonaktiebolaget Lm Ericsson (Publ) Détection d'erreurs assistée par le réseau pour l'intelligence artificielle sur une interface radio
WO2023240546A1 (fr) * 2022-06-16 2023-12-21 北京小米移动软件有限公司 Procédés et appareil de surveillance de modèle, dispositif et support
CN116846763A (zh) * 2023-07-07 2023-10-03 北京佰才邦技术股份有限公司 模型监控的交互方法和系统、通信装置
CN117378237A (zh) * 2023-07-10 2024-01-09 北京小米移动软件有限公司 通信方法、终端、网络设备以及通信系统

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